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In this paper, we propose a content-boosted modified termite colony optimisation-based rating prediction algorithm (CMTRP) for group recommendation system. CMTRP employs a hybrid recommendation framework with respect to the big data paradigm to deal with the trend of large data. The framework utilises the communal ratings that help to overcome the scalability problem. The experimental results reveal that CMTRP provides less error in the rating prediction and higher recommendation precision compared with the existing algorithms. <\/jats:p>","DOI":"10.1142\/s0219649218500193","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T05:02:54Z","timestamp":1524546174000},"page":"1850019","source":"Crossref","is-referenced-by-count":9,"title":["Hybrid Group Recommendation Using Modified Termite Colony Algorithm: A Context Towards Big Data"],"prefix":"10.1142","volume":"17","author":[{"given":"Arup","family":"Roy","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Jharkhand 814142, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soumya","family":"Banerjee","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Jharkhand 814142, 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